lncAPNet is an extension of the Activity PASNet (APNet) framework that incorporates long non-coding RNAs (lncRNAs) into explainable, network-based patient stratification and supervised clustering using transcriptomic data.
This repository hosts the implementation and resources for the lncAPNet computational pipeline.
lncAPNet is a novel computational pipeline built upon the recently reported Activity PASNet (APNet) framework
(Gavriilidis et al., 2025).
It performs explainable AI (XAI)-driven supervised clustering of patient cohorts by integrating:
- Transcriptomic analysis
- Biological priors
- The concept of differential activity
The original APNet framework integrates:
- SJARACNe for gene regulatory network inference
- NetBID2 for driver activity analysis
- PASNet, a sparse and interpretable deep learning architecture for patient classification
These components are supported by biological priors and post hoc visualization modules for clinical bioinformatics applications.
lncAPNet expands the APNet toolbox by explicitly modeling lncRNA-driven regulatory mechanisms. The key extensions include:
- Graph-based, nonlinear interpretation of lncRNA–mRNA regulatory relationships using the SJARACNe / NetBID2 framework
- Integration of an lncRNA-specific knowledge graph (lncRNAlyzr-KG) into the biological priors
(Evangelista et al., 2025) - Identification and interpretation of lncRNA-associated regulons derived from NetBID2 analysis
- Enhanced explainability of lncRNA contributions to patient stratification and disease mechanisms
🚧 Under active development
- Installation instructions: Under construction
- A Nextflow-based implementation of the lncAPNet pipeline is planned for future release to improve scalability, portability, and reproducibility.
Under construction